Implementation of particle swarm optimization algorithm for estimating the innovative parameters of a spike sequence from noisy samples via maximum likelihood method

Hamed Sadjedi | Meisam Najjarzadeh

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URL :   http://research.shahed.ac.ir/WSR/WebPages/Report/PaperView.aspx?PaperID=148035
Date :  2020/11/20
Publish in :    Digital Signal Processing: A Review Journal
DOI :  https://doi.org/10.1016/j.dsp.2020.102799
Link :  http://dx.doi.org/10.1016/j.dsp.2020.102799
Keywords :Finite rate of innovation signalsSpike sequenceSampling and reconstructionModified particle swarm optimizationMaximum likelihood estimation

Abstract :

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Since the introduction of finite rate of innovation (FRI) signals in 2002, various deterministic and stochastic techniques have been proposed to estimate the innovative parameters of FRI signal, e.g. time instants tk and weights ck belonging to a linear combination of finite number of Diracs, from its noisy samples regardless of sampling kernel’s type. Having analyzed the Bayesian methods dedicated to the retrieval of signal innovations, particularly the IterML algorithm introduced by Wein & Srinivasan, we discover some limitations which still leave room for improvement. In this article, we present a novel stochastic hybrid algorithm utilizing both maximum–likelihood-estimation (MLE) and Modified Particle Swarm Optimization (MPSO) in order to improve upon IterML in terms of robustness to noise and accuracy of estimated parameters. Relying on extensive simulations, our proposed algorithm provably achieves better performance than IterML while maintaining comparable computational cost. Due to high dependency of this problem on the trade-off between the level of noise and the number of samples, we also investigate this compromise in order to achieve better reconstruction error metrics than IterML